Systems and methods for monitoring and controlling industrial processes

ABSTRACT

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for monitoring and/or controlling processing parameters for an industrial process. In various embodiments, a method is provided that comprises: receiving media of a processing region of an industrial process that comprises an object, wherein the media comprises media elements, and each media element comprises a field of view of the object; identifying a set of pixels found in the area of interest; and for each media element: extracting an attribute value from each pixel found in the media element; constructing an attribute profile comprising the attribute value for each pixel; mapping the attribute profile to a mapped profile that comprises at least one property value that correlates to at least one attribute value; and providing the mapped profile to a control system to use in controlling processing parameters of the industrial process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 63/394,805 filed Aug. 3, 2022, which is herebyincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure is generally related to data processing systemsand methods for the automated analysis of media or recognition of apattern for the purpose of monitoring and/or controlling industrialprocesses.

BACKGROUND

Industrial processes, such as processes used in manufacturing items(e.g., food, consumer goods, chemicals, etc.), often include complexmanufacturing equipment, assembly equipment, fabrication equipment,and/or the like operating with tight tolerances. In addition, suchequipment may also operate at high speed, such as for mass-produceditems. In many cases, entities, such as manufacturers, who areperforming these industrial processes will implement still imagesurveillance equipment to monitor the equipment used within theseindustrial processes and/or items produced by these industrial processesthat can prove to present technical challenges in identifying andremedying malfunctioning of the equipment and/or damaging of itemsduring performance of the industrial processes. For example, a foodmanufacturer may perform quality assurance checks of completed foodpackages by using an automated camera and image processing system toidentify malformed or damaged items. However, although such a system maybe able to detect large problems in individual items, still imagesgenerated by these systems often fail to reveal variations over time inthe items (e.g., variations in the properties of the items), thuspreventing diagnosis and remediation of manufacturing process issuesand/or item issues.

In other cases, entities may use closed-circuit television systems tomonitor equipment used in the industrial processes and/or items producedby these industrial processes for the purpose of detectingmalfunctioning equipment and/or damaging of items. However, theseclosed-circuit television systems also present technical challenges inthat the real-time surveillance provided through these systems may failto reveal gradual variations over time in a manufacturing process, orminor variations in rapid processes. For example, an arm of a machinemay sporadically shift over time, such that an observer (e.g., a human)watching a video produced in real-time through a closed-circuittelevision system may find it very difficult to notice variations inmovement. In another example, a component of a manufacturing process maymove with a certain frequency such that a frame rate produced by areal-time surveillance system that is too slow and/or alias with thefrequency may prevent an observer from detecting abnormal componentmovement.

In addition to monitoring, entities, such as manufacturers, who areperforming these industrial processes may also implement control systemsfor measuring properties of equipment components and/or items beingmanufactured during performance of the industrial processes for thepurpose of using the measurements of the properties in controlling theequipment. Again, these control systems can present technical challengesin that the control systems can often operate at too slow of a rate totimely correct processing parameters of the equipment, leading to themanufacturing of defective items at a large quantity.

For example, equipment used in manufacturing paper may include a set ofactuators that feeds pulp to the equipment. In addition, the equipmentmay also include one or more steam boxes to reduce the paper moisture byincreasing the sheet temperature. Here, an entity operating theequipment may use a quality control system (QCS) to control theactuators and/or steam boxes to ensure uniform distribution (profiles)of several properties that define the specification of a given papergrade for the paper manufactured by the equipment. The equipment mayinclude multiple scanners that use different scanner configurations tomeasure properties important to the process at given locations.

However, a scanner can often take ten to thirty seconds to provide afull width profile for a measured property. As a result, the QCS mayreceive the measurements of the properties (e.g., the full widthprofiles) at too slow of a rate that can result in manufacturing ofdefective paper at a significant quantity due to delayed controladjustments made to the actuators and/or steam boxes. Accordingly, thereis a need for systems and methods that aid in timely identification ofdeviations from baseline movements of components of equipment and/oritems produced through manufacturing and other industrial processes.

SUMMARY

In general, various embodiments of the present disclosure providemethods, apparatuses, systems, computing devices, computing entities,and/or the like for monitoring and/or controlling one or more processingparameters for an industrial process. In accordance with variousembodiments, a method is provided that comprises: receiving, bycomputing hardware, media of a processing region of an industrialprocess, wherein: the processing region comprises at least one object,the media comprises a plurality of media elements, and each mediaelement of the plurality of media elements comprises a field of view ofthe at least one object; identifying, by the computing hardware andbased on an area of interest, a set of pixels, wherein the field of viewcomprises the area of interest; for each media element of the pluralityof media elements: extracting, by the computing hardware, an attributevalue from each pixel of the set of pixels found in the media element;and constructing, by the computing hardware, a respective arraycomprising each attribute value; combining, by the computing hardware,each of the respective arrays in a data structure; and analyzing, by thecomputing hardware, the data structure to provide data on a processingparameter associated with the industrial process.

In particular embodiments, analyzing the data structure comprisesfacilitating generation and transmission of a graphical representationof the data structure to a user device for display. In particularembodiments, the respective arrays are indexed in the data structureaccording to a sequence of the plurality of media elements found in themedia, and the graphical representation comprises a visualrepresentation displaying each respective array being arranged at leastsubstantially sequentially along an axis of the graphical representationaccording to how the respective arrays are indexed in the datastructure.

In particular embodiments, the media comprises an interstitial portion,and the method further comprises: determining, by the computinghardware, a beginning media element of the interstitial portion;determining, by the computing hardware, an ending media element of theinterstitial portion; and excluding, by the computing hardware, mediaelements between the beginning media element and the ending mediaelement from the plurality of media elements. In some embodiments,determining the beginning media element of the interstitial portioncomprises receiving a first trigger signal indicating an ending of amovement cycle of the at least one object; and determining the endingmedia element of the interstitial portion comprises receiving a secondtrigger signal indicating a beginning of the movement cycle of the atleast one object. In some embodiments, determining the beginning mediaelement of the interstitial portion comprises detecting a first changein the attribute value for a particular pixel of the set of pixels; anddetermining the ending media element of the interstitial portioncomprises detecting a second change in the attribute value for theparticular pixel of the set of pixels. In some embodiments, the secondchange corresponds to the beginning media element of a processingportion, the first change corresponds to the ending media element of theprocessing portion, and the method further comprises: determining, bythe computing hardware, an elapsed time of the processing portion;removing, by the computing hardware, media elements of the plurality ofmedia elements based at least in part on the elapsed time being greaterthan a baseline processing time; and adding, by the computing hardware,media elements to the plurality of media elements based at least in parton the elapsed time being less than the baseline processing time.

In accordance with various embodiments, a system is provided comprisinga non-transitory computer-readable medium storing instructions and aprocessing device communicatively coupled to the non-transitorycomputer-readable medium. The processing device is configured to executethe instructions and thereby perform operations comprising: receivingmedia of a processing region involving processing of an object, wherein:the media comprises a plurality of media elements, and each mediaelement of the plurality of media elements comprises a field of view ofthe object; identifying, based on an area of interest, a set of pixels,wherein the field of view comprises the area of interest; for each mediaelement of the plurality of media elements: extracting an attributevalue for the object from the set of pixels found in the media element;and constructing a respective array comprising each attribute value;combining each of the arrays into a data structure; and analyzing thedata structure to provide data on a property of the object.

In particular embodiments, the media is received at least substantiallyin real time from recording equipment, and the operations furthercomprise: receiving a speed measurement indicating a speed at which theobject is being processed; and adjusting a frame rate of the recordingequipment based on a difference between the speed measurement and abaseline speed. In particular embodiments, the operations furthercomprise: retrieving a template data structure representing a baselineattribute value; generating a difference data structure by subtractingthe data structure from the template data structure; and facilitatingtransmission of a graphical representation of the difference datastructure to a user device for display.

In particular embodiments, the operations further comprise: retrieving atemplate data structure representing baseline attribute value;generating a difference data structure by subtracting the data structurefrom the template data structure; and modifying an industrial processassociated with processing the object based on determining that anaspect of the difference data structure satisfies a threshold. In someembodiments, modifying the industrial process comprises at least one of:facilitating discarding production of the object; or facilitatingadjustment of a processing parameter of the industrial process.

In particular embodiments, the operations further comprise: identifyinga location of the object in each of a plurality of arrays; constructinga dataset comprising the locations and corresponding times; anddetermining a frequency of movement of the object by performing aFourier transform on the dataset. In particular embodiments, the systemfurther comprises at least one motion sensor communicatively coupled tothe processing device, and the operations further comprise: determininga beginning media element of an interstitial portion based on a firsttrigger signal from the at least one motion sensor, the first triggersignal indicating an end of a movement cycle of the object; determiningan ending media element of the interstitial portion based on a secondtrigger signal from the at least one motion sensor, the second triggersignal indicating a beginning of the movement cycle of the object; andexcluding media elements between the beginning media element and theending media element from the plurality of media elements.

In accordance with various embodiments, a non-transitorycomputer-readable medium storing computer-executable instructions isprovided. The computer-executable instructions, when executed bycomputing hardware, configure the computing hardware to performoperations comprising: receiving media of an industrial process,wherein: the media comprises a plurality of media elements, and eachmedia element of the plurality of media elements comprises a field ofview of at least one object; identifying a set of pixels within thefield of view; for each media element of the plurality of mediaelements: extracting an attribute value for the at least one object fromthe set of pixels found in the media element; and constructing arespective array comprising each attribute value; combining each of thearrays into a data structure; and analyzing the data structure toprovide data on a processing parameter associated with the at least oneobject.

In particular embodiments, the at least one object comprises at leastone of a component of equipment or an item being manufactured. Inparticular embodiments, analyzing the data structure comprisesfacilitating generation and transmission of a graphical representationof the data structure to a user device for display. In particularembodiments, the respective arrays are indexed in the data structureaccording to a sequence of the plurality of media elements found in themedia, and the graphical representation comprises a visualrepresentation displaying each respective array being arranged at leastsubstantially sequentially along an axis of the graphical representationaccording to how the respective arrays are indexed in the datastructure.

In particular embodiments, the operations further comprise: retrieving atemplate data structure representing baseline attribute value;generating a difference data structure by subtracting the data structurefrom the template data structure; and modifying the industrial processbased on determining that an aspect of the difference data structuresatisfies a threshold. In some embodiments, modifying the industrialprocess comprises at least one of: facilitating discarding production ofthe at least one object; or facilitating adjustment of the processingparameter of the industrial process.

In accordance with various embodiments, a method is provided thatcomprises: receiving, by computing hardware, media of a processingregion of an industrial process, wherein: the processing regioncomprises at least one object, the media comprises a plurality of mediaelements, and each media element of the plurality of media elementscomprises a field of view of the at least one object; identifying, bythe computing hardware and based on an area of interest, a set ofpixels, wherein the field of view comprises the area of interest; andfor each media element of the plurality of media elements: extracting,by the computing hardware, an attribute value from each pixel of the setof pixels found in the media element; constructing, by the computinghardware, an attribute profile comprising the attribute value for eachpixel of the set of pixels; mapping, by the computing hardware, theattribute profile to a mapped profile, wherein the mapped profilecomprises at least one property value that correlates to at least oneattribute value of the attribute profile; and providing, by thecomputing hardware, the mapped profile to a control system, wherein thecontrol system uses the mapped profile in controlling one or moreprocessing parameters of the industrial process.

In particular embodiments, the at least one object comprises at leastone of a component of equipment or an item being manufactured. In someembodiments, the industrial process comprises a manufacturing processfor paper, the at least one attribute value comprises a measure ofbrightness of at least one pixel of the set of pixels, the at least oneproperty value comprises a measure of a thickness of the paper, and theone or more processing parameters comprise an amount of pulp fed by oneor more actuators during the manufacturing of the paper. In someembodiments, the industrial process comprises a manufacturing processfor paper, the at least one attribute value comprises a measure oftemperature of at least one pixel of the set of pixels, the at least oneproperty value comprises a measure of moisture of the paper, and the oneor more processing parameters comprise an amount of steam provided byone or more steam boxes to a surface of the paper during themanufacturing of the paper.

In particular embodiments, the method further comprises: averaging, bythe computing hardware, the at least one attribute value found in theattribute profile constructed for each media element of the plurality ofmedia elements in a time domain to produce an average attribute value;and analyzing, by the computing hardware, the average attribute value todetermine a variation in the one or more processing parameters of theindustrial process. In some embodiments, the method further comprisesproviding data on the variation to personnel to use in identifying aproblem with equipment performing the industrial process.

In particular embodiments, mapping the attribute profile to the mappedprofile comprises using a rules-based model to map the at least oneattribute value to the at least one property value, and the rules-basedmodel uses at least one of a table, graph, or rules sets in identifyingthe at least one property value. In particular embodiments, the methodfurther comprises: identify, by the computing hardware, a correlationstrength that identifies how well the at least one attribute valuecorrelates to the at least one property value; and providing, by thecomputing hardware, the correlation strength along with the mappedprofile to the control system, wherein the control system determines,based on the correlation strength, to use the mapped profile incontrolling the one or more processing parameters of the industrialprocess.

In accordance with various embodiments, a system is provided comprisinga non-transitory computer-readable medium storing instructions and aprocessing device communicatively coupled to the non-transitorycomputer-readable medium. The processing device is configured to executethe instructions and thereby perform operations comprising: receiving amedia element of a processing region of an industrial process, wherein:the processing region comprises at least one object, and the mediaelement comprises a field of view of the at least one object;identifying, based on an area of interest, a set of pixels, wherein thefield of view comprises the area of interest; and extracting anattribute value from each pixel of the set of pixels found in the mediaelement; constructing an attribute profile comprising the attributevalue for each pixel of the set of pixels; and mapping the attributeprofile to a mapped profile, wherein the mapped profile comprises atleast one property value that correlates to at least one attribute valueof the attribute profile, and the at least one property value is used bya control system in controlling one or more processing parameters of theindustrial process.

In some embodiments, the industrial process comprises a manufacturingprocess for paper, the at least one attribute value comprises a measureof brightness of at least one pixel of the set of pixels, the at leastone property value comprises a measure of a thickness of the paper, andthe one or more processing parameters comprise an amount of pulp fed byone or more actuators during the manufacturing of the paper. In someembodiments, the industrial process comprises a manufacturing processfor paper, the at least one attribute value comprises a measure oftemperature of at least one pixel of the set of pixels, the at least oneproperty value comprises a measure of moisture of the paper, and the oneor more processing parameters comprise an amount of steam provided byone or more steam boxes to a surface of the paper during themanufacturing of the paper.

In particular embodiments, the operations further comprise providing themapped profile to the control system to use the mapped profile incontrolling the one or more processing parameters of the industrialprocess. In particular embodiments, mapping the attribute profile to themapped profile comprises using a rules-based model to map the at leastone attribute value to the at least one property value. In particularembodiments, the operations further comprise: identify a correlationstrength that identifies how well the at least one attribute valuecorrelates to the at least one property value; and providing thecorrelation strength along with the mapped profile to the controlsystem, wherein the control system determines, based on the correlationstrength, to use the mapped profile in controlling the one or moreprocessing parameters of the industrial process.

In accordance with various embodiments, a non-transitorycomputer-readable medium storing computer-executable instructions isprovided. The computer-executable instructions, when executed bycomputing hardware, configure the computing hardware to performoperations comprising: receiving a media element of a processing regionof an industrial process, wherein: the processing region comprises atleast one object, and the media element comprises a field of view of theat least one object; identifying, based on an area of interest, a set ofpixels, wherein the field of view comprises the area of interest;extracting an attribute value from each pixel of the set of pixels foundin the media element; and mapping at least one attribute value for atleast one pixel of the set of pixels to a mapped profile, wherein themapped profile comprises at least one property value that correlates tothe at least one attribute value, and the at least one property value isused by a control system in controlling one or more processingparameters of the industrial process.

In some embodiments, the industrial process comprises a manufacturingprocess for paper, the at least one attribute value comprises a measureof brightness of at least one pixel of the set of pixels, the at leastone property value comprises a measure of a thickness of the paper, andthe one or more processing parameters comprise an amount of pulp fed byone or more actuators during the manufacturing of the paper. In someembodiments, the industrial process comprises a manufacturing processfor paper, the at least one attribute value comprises a measure oftemperature of at least one pixel of the set of pixels, the at least oneproperty value comprises a measure of moisture of the paper, and the oneor more processing parameters comprise an amount of steam provided byone or more steam boxes to a surface of the paper during themanufacturing of the paper.

In particular embodiments, the operations further comprise providing themapped profile to the control system to use the mapped profile incontrolling the one or more processing parameters of the industrialprocess. In particular embodiments, mapping the at least one attributevalue to the mapped profile comprises using a rules-based model to mapthe at least one attribute value to the at least one property value. Inparticular embodiments, the operations further comprise: identifying acorrelation strength that identifies how well the at least one attributevalue correlates to the at least one property value; and providing thecorrelation strength along with the mapped profile to the controlsystem, wherein the control system determines, based on the correlationstrength, to use the mapped profile in controlling the one or moreprocessing parameters of the industrial process.

BRIEF DESCRIPTION OF THE DRAWINGS

In the course of this description, reference will be made to theaccompanying drawings, which are not necessarily drawn to scale, andwherein:

FIGS. 1A-1H provide an example of a representation of an analysis ofmovement of a component used within an industrial process in accordancewith various embodiments of the disclosure;

FIGS. 2A-2G provide an example of a representation of an analysis ofmovement of an item handled within an industrial process in accordancewith various embodiments of the disclosure;

FIGS. 3A-3G provide another example of a representation of an analysisof movement of an item handled within an industrial process inaccordance with various embodiments of the disclosure;

FIGS. 4A-4G provide an example of a representation of an analysis of achange in a property of an item handled within an industrial process inaccordance with various embodiments of the disclosure;

FIGS. 5A-5G provide another example of a representation of an analysisof a change in a property of an item handled within an industrialprocess in accordance with various embodiments of the disclosure;

FIG. 6 provides an example of a slice line of pixels that can be use inproviding a measurement profile in accordance with various embodimentsof the disclosure;

FIG. 7 provides an example of a measurement profile in accordance withvarious embodiments of the disclosure;

FIG. 8 provides an example of a mapped profile in accordance withvarious embodiments of the disclosure;

FIG. 9 depicts an example of a process for monitoring an industrialprocess in accordance with various embodiments of the disclosure;

FIG. 10 provides an example of sampling pixels in accordance withvarious embodiments of the disclosure;

FIG. 11 provides another example of sampling pixels in accordance withvarious embodiments of the disclosure;

FIG. 12 is a diagram illustrating an example of computing hardware thatcan used in accordance with various embodiments of the disclosure; and

FIG. 13 is a diagram illustrating an example of a system environment inwhich various embodiments of the disclosure may be implemented.

DETAILED DESCRIPTION

Various embodiments of the disclosure now will be described more fullyhereinafter with reference to the accompanying drawings. It should beunderstood that the disclosure may be embodied in many different formsand should not be construed as limited to the embodiments set forthherein. Rather, these embodiments are provided so that this disclosurewill be thorough and complete, and will fully convey the scope of theinvention to those skilled in the art. Like numbers refer to likeelements throughout.

For the purpose of this disclosure, the term “industrial process” maydescribe a process by which an item is handled. For example, “handling”an item can involve manufacturing or altering the item such asassembling the item, packaging the item, forming the item, stamping theitem, and/or the like. An industrial process may include, for example, aprocess to handle (e.g., manufacture and/or package) items such as foodor drinks. An industrial process may also include handling of non-edibleitems such as electronics, clothing, furniture, machinery, chemicals,etc. Further still, an industrial process may also include processes toimprove items, such as a painting process. An industrial process may bediscrete (e.g., producing one unit of an item at a time) or continuous(e.g., producing an item continuously, such as wire, yarn, orchemicals). Thus, in general, an industrial process may includeprocesses by which equipment (e.g., machine(s)) handles items in asubstantially repetitive manner.

In industrial processes, equipment components may move in order tohandle items, for instance in a periodic manner starting at a beginningposition, moving to perform an operation on an item, and returning to abeginning position to reperform the operation on a subsequent item. Insome cases, the process may require precise timing and positioning ofequipment components in order to produce consistent quality. Rapid massmanufacturing may heighten these requirements, which, if not met, mayresult in wasted items that do not comply with manufacturing tolerances.

For instance, an industrial process such as a compact disc manufacturingprocess may include operations to apply a label to a front side of thecompact disc with an arm. The arm may move between a starting positionto an application position, and back to the starting position in afraction of a second to maximize production rates. If the arm ismisaligned, mistimed, or otherwise falls out of manufacturingtolerances, the arm may cause manufacturing defects such as the labelsbeing applied incorrectly, which can result in a significant portion ofmanufactured discs being discarded. Similarly, if the compact discs,themselves, become misaligned, then the arm may apply the labelsincorrectly, which can also result in a significant portion ofmanufactured discs being discarded. Likewise, if properties orconditions of the compact discs change so that the surface of thecompact discs becomes warped or distorted, then the arm may apply thelabels incorrectly, which can result in a significant portion ofmanufactured discs being discarded.

However, diagnosing the cause of such manufacturing defects can bedifficult to perform. For example, diagnosing that the arm is applyinglabels mid-movement such that precise timing or flexing of the armduring accelerations of the application movement affects proper labelplacement can be difficult to perform. Further, collecting measurementsof certain properties of the arm and/or the disc to allow foradjustments to be made in controlling arm movement in a timely fashionto correct or avoid such manufacturing defects can be difficult toperform.

Accordingly, various embodiments of the present disclosure aid in thediagnostic and/or control process by providing systems and methods forvisualizing and analyzing movement of equipment (e.g., machinecomponents) and/or items during an industrial process by extractingfocused image data from media such as video, images, and/or the like.For example, FIGS. 1A-1H provide a representation of an analysis of anindustrial process that can be performed according to variousembodiments of the disclosure. Specifically, various embodiments of thedisclosure involve a method that can be performed to record sequentialelements of media to capture movement of one or more objects associatedwith an industrial process as the one or more objects pass through afield of view 100 of the recording equipment. For example, as shown inFIGS. 1A-1F, the method can involve recording sequential elements ofmedia to capture movement of an object such as an arm 106 secured to awall 102 by a hinge 104 that are part of an industrial process. Here,the method may involve using various types of recording equipment suchas, for example, visual cameras such as an area camera recordingsequential frames of video, a line scan camera recording sequential lineimages, and/or the like. In other instances, the method may involveusing other types of recording equipment such as, for example,non-visual cameras such as a short-wave infrared camera, a mid-waveinfrared camera, a long-wave infrared camera, and/or the like.

In the example shown in FIGS. 1A-1F, the method is used in recording thesequence of media elements demonstrating the arm 106 rotating about thehinge 104. The arm 106 begins in a position that is essentiallyperpendicular to the wall 102, as shown in FIG. 1A, swings downapproximately forty-five degrees, as shown in FIG. 1B, and returns to aposition that is essentially perpendicular to the wall 102, as shown inFIG. 1C. Further, the arm 106 continues to swing up approximatelyforty-five degrees, as shown in FIG. 1D. Subsequently, the arm 106returns to a position that is essentially perpendicular, as shown inFIG. 1E, to restart the rotation cycle, as shown in FIG. 1F. Thus, thearm 106 in this simplified and exaggerated example rotates up and downabout the hinge 104 periodically.

In various embodiments, the method involves recording the arm 106,throughout its movement, as the arm passes through an area of interest108 that lies within the field of view 100. For example, an operator mayindicate the area of interest 108 by making a selection of pixels withinthe field of view 100 that captures the movement of the arm 106.Accordingly, the area of interest 108 can be composed of various shapes,configurations, sizes, and/or the like. For example, the area ofinterest 108 shown in FIGS. 1A-1F is represented as a rectangle (e.g., aline of pixels).

In various embodiments, the method involves assembling one or moreattribute values (e.g., brightness, color, etc.) gathered from pixels ofthe media that are found in the area of interest 108 into one or moregraphical representations 110 of the movement of the one or moreobjects. In some embodiments, the method may involve arranging attributevalues of the position of the one or more objects as the one or moreobjects pass through the area of interest. For example, the method mayinvolve assembling media elements (e.g., video frames) of the positionsof the arm 106 shown in FIGS. 1A to 1F as the arm 106 passes through thearea of interest. In this example, the method may involve assembling agraphical representation, as shown in FIG. 1G, of a repeated pattern ofthe first set of pixels (e.g., left-most mark) that illustrates the arm106 shown in the area of interest 108 in FIG. 1A that is essentially ina horizontal position and substantially centered in the area of interest108.

In some embodiments, the method may involve arranging attribute valuesof pixels from subsequent frames sequentially in a representation of theperiodic movement of the one or more objects as the one or more objectsmove through the area of interest. For example, the method may involveassembling media elements (e.g., video frames) of the periodic movementof the arm 106 shown in FIGS. 1A to 1F as the arm 106 moves through thearea of interest. In this example, the method may involve assembling agraphical representation 110, as shown in FIG. 1G, that illustrates theperiodic movement of the arm 106 as a middle mark, a lower mark, amiddle mark, an upper mark, a middle mark, and a lower mark,respectively, that correspond to the media elements (e.g., video frames)illustrated in FIGS. 1A, 1B, 1C, 1D, 1E, and 1F, respectively.

In some embodiments, the method may involve arranging attribute valuesof pixels from subsequent frames sequentially in a representation of amovement cycle of the one or more objects. For example, the method caninvolve assembling media elements (e.g., video frames) of the periodicmovement of the arm 106 shown in FIGS. 1A to 1F as the arm 106 movesthrough the area of interest. In this example, the method may involveassembling a graphical representation 110, as shown in FIG. 1H, thatillustrates the periodic movement of the arm 106 in a wave motion (e.g.,a sine wave motion). Accordingly, the graphical representations shown inFIGS. 1G and 1H can provide the movement, periodic movement, and/ormovement cycle of the arm 106, and may appear similar to a graphdepicting the position of the arm 106 over time. In some instances, anoperator may define multiple areas of interest. In these instances, themethod may involve assembling multiple graphical representations of themovement, allowing a comparison of the movement between multipleobjects.

Accordingly, an operator may use a graphical representation of themovement of one or more objects in determining problems, errors,defects, and/or the like in the operation (e.g., the movement) of theone or more objects involved in the industrial process. In otherinstances, an automated process may be performed that uses a graphicalrepresentation of the movement of one or more objects in determiningproblems, errors, defects, and/or the like in the operation of the oneor more objects. For example, an operator or automated process may use agraphical representation of the movement of the arm 106 (e.g., pixelarrangements thereof shown in the representation) in determining thatthe arm 106 does not complete a full movement cycle (e.g., does notfully rotate upward), deviates from a baseline movement frequency (e.g.,slower than the baseline movement frequency), jitters during movement(e.g., does not have a smooth movement), and/or the like.

Thus, various embodiments of the disclosure can overcome severaltechnical challenges encountered in using conventional processes todetermine errant movements of one or more objects involved in industrialprocesses. For example, various embodiments of the disclosure canprovide a graphical representation of the movement of one or moreobjects that can facilitate detection of errant movements more quicklyover conventional processes such as conventional processes that involvean operator tediously and slowly progressing through a video attemptingto compare individual frames in their entirety to detect errantmovements. Moreover, various embodiments of the disclosure can provide agraphical representation of the movement of one or more objects that canfacilitate detection of errant movements more effectively overconventional processes where the movement of the one or more objectsinvolves an extended movement cycle (e.g., a movement cycle where athousand frames may lie between a beginning of a cycle and a beginningof the next cycle).

In additional or alternative embodiments, the method can involvecarrying out the same analysis with respect to the movement of itemsbeing handled (e.g., manufactured) within an industrial process. Forexample, the process may involve carrying out an analysis to identify achange in movement of items as they are processed through a particulararea, part, portion, and/or the like of the industrial process. FIGS.2A-2G provide an example of a representation of an analysis of aparticular item 206 moving through an industrial process according tovarious embodiments. Here, the method may involve capturing particularmovement of the item 206 through the industrial process as the item 206passes through the area of interest 208 that lies within the field ofview 200. FIGS. 2A-2F illustrate sequential elements of media (e.g.,sequential frames and/or images) capturing movement of the item 206 asthe item 206 moves through a particular area, part, portion, and/or thelike of the industrial process.

In some embodiments, the method may involve recording one or moreattribute values (e.g., brightness, color, etc.) from pixels in the areaof interest 208 and assembling the one or more attribute values into oneor more graphical representations of the movement of the item 206. Forexample, the method may involve assembling the one or more attributevalues into the graphical representation 210 shown in FIG. 2G of themovement of the item 206 through the area, part, portion, and/or thelike of the industrial process. In additional or alternativeembodiments, the method may involve conducting a comparison of graphicalrepresentations of the movement of different items 206 to identify achange in the movement of the items 206 as they are processed throughthe area, part, portion, and/or like of the industrial process.

For example, FIGS. 3A-3F illustrate an example of sequential elements ofmedia (e.g., sequential frames and/or images) capturing movement of asecond, different item 306 as the second item 306 moves through theparticular area, part, portion, and/or the like of the industrialprocess. Here, movement of the item 306 is captured, as shown in thesequence of media elements of FIGS. 3A-3F, as the item 306 passesthrough the area of interest 208 that lies within the field of view 200.In this instance, the second item 306 is moving at an angle, as opposedto the first item 206 that moved more in a straight line through theparticular area, part, portion, and/or the like of the industrialprocess. Therefore, the method may involve assembling a graphicalrepresentation 310, as shown in FIG. 3G, of the movement of the second,different item 306, and then comparing the graphical representation 210of the movement of the first item 206 to the graphical representation310 of the movement of the second item 306 to detect that the movementof the items 206, 306 has changed through the particular area, part,portion, and/or the like of the industrial process.

Thus, various embodiments of the disclosure can be used in monitoringand/or analyzing positioning and/or movement of items in a process suchas, for example, monitoring and/or analyzing a location and arrangementof a series of items during manipulation by equipment components.Accordingly, the method can be used in various embodiments to performsuch an analysis in helping diagnose item characteristics affecting howan equipment component interacts with the items during manufacture.

In additional or alternative embodiments, the method may involvecarrying out the same analysis with respect to properties of items beinghandled within an industrial (e.g., manufacturing) process. For example,the process may involve carrying out the analysis to identify a changewith respect to a property of items that are handled within anindustrial process. FIGS. 4A-4G provide an example of a representationof an analysis of a property of a particular item 406 moving through anindustrial process according to various embodiments. In this example,the method involves monitoring the property with respect to a patternassociated with the items as they pass through the industrial process.Here, for example, the pattern may involve a quality, texture, shape,and/or the like of the surface of the items. As shown in the sequence ofmedia elements of FIGS. 4A-4F, the method involves capturing the item406 as the item 406 passes through an area of interest 408 that lieswithin the field of view 400 as the item 406 moves through theindustrial process. In various embodiments, the method may involverecording attribute values (e.g., brightness, color, etc.) from pixelsin the area of interest 408 and assembling the attribute values into agraphical representation 410 representing the property (e.g., thepattern) of the item 406, as shown in FIG. 4G. In some embodiments, themethod may involve assembling and comparing graphical representations410 representing the property (e.g., the pattern) of other items 406that pass through the industrial process to identify a change in theproperty (e.g., the pattern) of the items 406.

For example, FIGS. 5A-5F illustrate an example of the sequentialelements of media (e.g., sequential frames and/or images) capturing thepattern of a second, different item 506 as the item 506 moves throughthe particular area, part, portion, and/or the like of the industrialprocess. Here, the pattern of the item 506 is captured, as shown in thesequence of media elements of FIGS. as the item 506 passes through thearea of interest 408 that lies within the field of view 400 as the item506 moves through the industrial process. In this instance, the patternon the second item 506 is different than the pattern on the first item406 that moved through the particular area, part, portion, and/or thelike of the industrial process. Therefore, the method may involveassembling a graphical representation 510 representing the pattern ofthe second, different item 506, as shown in FIG. 5G, and comparing thegraphical representation 410 representing the pattern of the first item406 with the graphical representation 510 of the pattern of the seconditem 506 to detect that the pattern (e.g., surface texture) on the items406, 506 has changed.

Thus, various embodiments of the disclosure can be used in monitoringand/or analyzing properties of items in a process such as, for example,monitoring and/or analyzing values, characteristics, patterns, and/orthe like of a property for a series of items during manipulation bymachine components. Accordingly, the method can be used in variousembodiments to perform such an analysis in helping diagnose itemproperties, characteristics, and/or the like affecting how an equipmentcomponent interacts with items during manufacture.

In additional or alternative embodiments, the method may involvecapturing one or more attribute values for pixels with respect to mediarecorded for one or more equipment components and/or items being handledwithin an industrial (e.g., manufacturing) process that correlate to oneor more properties used in controlling one or more processing parametersof the industrial process. As previously noted, an entity may wish tomeasure certain properties of equipment components and/or items beinghandled (referred to as objects) during performance of an industrialprocess for the purpose of using the measurements to control theequipment.

For example, equipment used in manufacturing paper may include a set ofactuators that feeds pulp to the equipment. In addition, the equipmentmay also include one or more steam boxes after the press section of theequipment to reduce the paper moisture by increasing the sheettemperature. These steam boxes can be non-profiling and/or profiling. Anon-profiling steam box applies steam evenly across the entire width ofthe equipment. A profiling steam box is divided into sections across thewidth of the equipment and the steam flow to each section can beadjusted to produce a uniform CD (cross direction) moisture profile.

In many cases, an entity operating the equipment will use a qualitycontrol system (QCS) to control the actuators and/or steam boxes toensure uniform distribution (profiles) of several properties that definethe specification of a given paper grade for the paper manufactured bythe equipment. For example, the QCS may use properties such as moisture,caliper (thickness), and/or basis weight (paper weight). The entity mayuse one or more scanners to measure these properties. For example, theequipment may include multiple scanners that use different scannerconfigurations to measure properties important to the process at givenlocations along the manufacturing process. Here, for example, each ofthe scanners may have a measurement head travelling across the paperweb, and the measurement head may have various sensors that measuredifferent attributes.

In various embodiments, the method involves extracting a set of pixelsfrom media recorded of one or more monitored objects (e.g., one or moreequipment components and/or items). In some instances, the method mayinvolve extracting multiple sets of pixels from multiple media recordedon the one or more monitored objects. For example, the method mayinvolve extracting the multiple sets of pixels from media recorded bymultiple recording equipment located at different points, locations,and/or the like along the industrial process. As a specific example, themethod may involve extracting a first set of pixels from media recordedon the one or more monitoring objects using a video camera at a firstlocation along the industrial process and a second set of pixels frommedia recorded on the one or more monitoring objects using an infraredcamera at a second location along the industrial process.

In addition, the method may further involve generating one or moreattribute profiles from the sets of pixels. For example, the method mayinvolve generating a first attribute profile based on attribute values,such as color, brightness, etc., extracted from a first set of pixels.In addition or alternatively, the method may involve generate a secondattribute profile based on attribute values, such as temperature,reflection, etc., extracted from a second set of pixels.

Accordingly, the attribute profiles may have either a linear or anon-linear correlation to mapped profiles of measurements for one ormore properties used by the entity in controlling the one or moreprocessing parameters of the industrial process. For example, the methodmay involve generating a brightness profile from extracting brightnessvalues from a set of pixels found in media recorded of a paper web usinga video camera that may correlate to a profile of thickness measurementsnormally generated by a caliber gauge during manufacturing of paper.Likewise, the method may involve generating a temperature profile fromextracting temperature value from a set of pixels found in mediarecorded of the paper web using an infrared camera that may correlate toa profile of moisture measurements normally taken by a moisture sensorduring manufacturing of paper. Accordingly, the one or more attributeprofiles generated from the set of pixels can be mapped to profiles(referred to as mapped profiles) of the properties used in controllingthe one or more processing parameters of the industrial process. Thesemapped profiles can then be used in controlling the one or moreprocessing parameters.

In particular embodiments, the method may involve performing the mappingof the attribute profiles (attribute values therein) to the mappedprofiles, and then providing the mapped profiles to the QC S to be usedin controlling the one or more processing parameters of the industrialprocess. In additional or alternative embodiments, the method mayinvolve providing the attribute profiles to the QCS, and the QCS thenperforms the mapping of the attribute profiles to the mapped profilesfor use in controlling the processing parameters of the industrialprocess.

Therefore, returning to the example involving manufacturing paper, theequipment used in manufacturing the paper may have a set of actuatorsthat feeds pulp to the equipment, as well as one or more steam boxesused to reduce paper moisture by increasing the sheet temperature. Here,an entity operating the equipment may be using a QCS to control theactuators and/or steam boxes to ensure uniform distribution (profiles)of several properties that define the specification of a given papergrade for the paper manufactured by the equipment such as moisture,caliper (thickness), and/or basis weight (paper weight).

In this example, the method may involve initially defining one or moreslice lines 600 for the paper web 610 that are perpendicular to the webmovement 615 and spanning between both edges of the paper web 610, asshown in FIG. 6 . For example, each of the slice lines 600 may beassociated with a camera recording media and positioned at a particularlocation along the manufacturing process. For example, a first sliceline 600 may be defined for a first camera positioned at a locationdownstream in the manufacturing process from the set of actuators and asecond slice line 600 may be defined for a second camera positioned at alocation downstream in the manufacturing process from the one or moresteam boxes.

Continuing, the method may involve extracting brightness values from afirst set of pixels defined by the first slice line 600 from mediarecorded on the paper web to generate a brightness profile thatrepresents the brightness distribution across the paper web. Inaddition, the method may involve extracting temperature (heat) valuesfrom a second set of pixels defined by the second slice line 600 frommedia recorded of the paper web to generate a temperature (heat) profilethat represents the temperature distribution across the paper web. Forexample, the one or more slice lines 600 may span fifty pixels, andproduce an attribute profile similar to the profile 700 shown in FIG. 7.

At this point, the method may involve mapping the attribute profiles tomapped profiles that can be used in controlling the actuators and/orsteam boxes. For example, the method may involve mapping the brightnessprofile to a correlated thickness profile that represents a thicknessdistribution across the paper web. Likewise, the method may involvemapping the temperature profile to a correlated moisture profilerepresenting a moisture distribution across the paper web. For example,assuming there are five actuators, the method may involve mapping thebrightness profile to a mapped profile 800 with values corresponding tothe average, minimum, maximum, median, and/or the like pixel values ofall pixels mapped to a given actuator as shown in FIG. 8 .

In some embodiments, the method may involve providing the mappedprofiles (e.g., the thickness profile and the moisture profile) to theQCS so that the QCS can use the mapped profiles in controlling theactuators and/or steam boxes. In other embodiments, the method mayinvolve providing the attribute profiles (e.g., the brightness profileand the temperature profile) to the QSC to map the attribute profiles tothe mapped profiles and then use the mapped profiles in controlling theactuators and/or steam boxes.

For example, the method may involve providing the mapped (thicknessand/or moisture) profiles for the actuators and/or steam boxes inreal-time so that the profiles can be continuously displayed and/or usedfor controlling the actuators and/or steam boxes in between scannercycles. In addition, the method may involve processing the mappedprofiles to alarm on deviations from a uniform profile. Here, suchalarms may be used to control one or more processing parameters foundafter the corresponding camera location. For example, one or more mappedprofiles may be compared to a uniform profile to detect coater wetstreaks and process an alarm. Accordingly, the alarm may lead totriggering the opening of a calendar nip to prevent calendar sheetbreaks.

In another example, the method may involve continuously monitoringattribute and/or mapped profiles to detect problems, issues, and/or thelike within the industrial process. As a specific example, the methodmay involve continuously monitoring subsequent temperature profiles todetect an uneven temperature distribution in the cross direction and/ormachine direction. Here, the uneven temperature distribution may signalissues with felts, rolls, dryer cans, and/or the like.

In particular embodiments, the method may involve averaging theattribute profiles in the time domain where the attribute value for eachpixel is averaged over several media elements (e.g., frames). Inaddition, the method may involve analyzing the individual points on anattribute profile or each attribute profile in the time domain todetermine variations in the direction of the web movement. Suchvariations can be used, for example, in identifying issues withequipment prior to the corresponding camera location.

Accordingly, the method in various embodiments can provide the mappedprofiles needed to control the one or more processing parameters of theindustrial process at a faster rate than conventional control systemscan provide correlating profiles. As a result, various embodiments ofthe method help to address the technical challenges that can beencountered by entities in using controls systems that operate at tooslow of a rate to timely correct the processing parameters, and avoidthe manufacturing of defective items at a large quantity.

Note that embodiments of the method may be used in various otherindustrial environments for the same purpose of controlling one or moreprocessing parameters of an industrial process. For example, embodimentsof the method may be utilized in the steel industry. As a specificexample, embodiments of the method may be used in galvanized steelproduction to control the spray nozzles on the zinc bath used inapplying the zinc to the steel. More specifically, embodiments of themethod may be used in capturing reflective attributes from mediarecorded of the coated surface of the steel that correlates to thicknessproperties of the zinc coating that can be used in controlling the spraynozzles. In another example, embodiments of the method may be used inthe automotive industry. As a specific example, embodiments of themethod may be used in automotive stamping operations to controlprocessing parameters of the stamping press. More specifically,embodiments of the method may be use in capturing movement attributesfrom media recorded of an arm of a stamping press placing blanks intothe press that correlates to a stamping cycle property for the pressthat can be used in controlling the pressure plates for the press.Accordingly, embodiments of the method can be used in other industrialenvironments that will be apparent to those of ordinary skill in the artin light of this disclosure.

Industrial Process Monitoring Module

Turning now to FIG. 9 , additional details are provided regarding anindustrial process monitoring module 900 for monitoring an industrialprocess in accordance with various embodiments of the disclosure. Forinstance, the flow diagram shown in FIG. 9 may correspond to operationscarried out, for example, by computing hardware as described herein, asthe computing hardware executes the industrial process monitoring module900.

In various embodiments, the industrial process monitoring module 900 maybe used for monitoring one or more processing parameters associated withan industrial process and generating data on the one or more processingparameters to assist in diagnosing any defects, errors, problems, and/orthe like that may be occurring with respect to the industrial process.For example, the module 900 may be used to construct a timing diagram,such as the graphical representations 110, 210, 310, 410, 510 shown inFIGS. 1G, 2G, 3G, 4G, and 5G. In additional or alternative embodiments,the industrial process monitoring module 900 may be used for monitoringone or more properties of objects associated with an industrial processto be used in controlling one or more processing parameters of theindustrial process. For example, the module 900 may be used to constructan attribute profile and/or a correlating mapped profile, as shown inFIGS. 7 and 8 , that can be used in controlling the one or moreprocessing parameters of the industrial process.

The process involves the industrial process monitoring module 900receiving media at Operation 902. For example, the media may involve avideo, images, and/or the like of a processing region of an industrialprocess, in which the media comprises a field of view. The media may beprovided in real-time (e.g., live-streamed) as the industrial process isbeing performed, or may be provided after the industrial process hasbeen performed such as, for example, the media may be a recorded mediathat is uploaded from a storage medium.

At Operation 904, The industrial process monitoring module 900identifies an area of interest found within at least a portion of thefield of view. For example, the area of interest may be a line of pixels(e.g., one pixel wide), a square of pixels (e.g., a set number ofpixels), a rectangle of pixels (e.g., multiple pixels wide), and/or thelike. In some embodiments, the industrial process monitoring module 900receives an indication from an operator who identifies the area ofinterest by drawing the area on a graphical user interface as an overlayof the media. In other embodiments, the industrial process monitoringmodule 900 identifies the area of interest through another source suchas metadata, a profile, and/or the like provided along with the media.

At Operation 906, the industrial process monitoring module 900determines a set of pixels corresponding to the area of interest. Forexample, the module 900 may perform this particular operation bydetermining the set of pixels underlying the overlay provided by theoperator or some other source at Operation 904. In some embodiments, theindustrial process monitoring module 900, or some other module, maystore identification of the pixels associated with the overlay (e.g.,grid locations, etc.) in a memory to assist in determining the set ofpixels.

At Operation 908, the industrial process monitoring module 900 continueswith performing an iterative process of analyzing the set of pixelsthrough a plurality of media elements (e.g., frames, images, stills,and/or the like) of the media. In various embodiments, the industrialprocess monitoring module 900 performs an iteration of the iterativeprocess by determining one or more attribute values for each pixel ofthe set of pixels in a particular media element, such as, for example, abrightness, a color, an intensity, a temperature, etc. In addition, theindustrial process monitoring module 900 may determine a numericalrepresentation of the brightness, color, intensity, temperature, etc.Next, the industrial process monitoring module 900 continues theiteration at Operation 910 with constructing a respective array for themedia element comprising each of the one or more attribute values foreach pixel in the set of pixels. For example, the area of interest mayinvolve a width of one pixel. Therefore, the industrial processmonitoring module 900 may construct the array as one-dimensional such asa column vector with each element of the vector providing one or morerepresentations (e.g., one or more numerical values) of the attribute(s)of the corresponding pixel. At Operation 912, the industrial processmonitoring module 900 determines if media elements remain for the media(i.e., if the video has unanalyzed portions remaining). If mediaelements remain, then the industrial process monitoring module 900returns to Operation 908 to analyze the next media element. If no mediaelements remain, then the industrial process monitoring module 900proceeds to Operation 914.

At Operation 914, the industrial process monitoring module 900 combineseach of the respective arrays for each of the media elements into a datastructure. In some embodiments, the industrial process monitoring module900 combines each of the respective arrays into a data structure that istwo-dimensional such as a matrix, with each column of the matrix holdingan array produced during Operations 908 and 910 for a particular elementof the media, and each row of the matrix corresponding to a particularpixel of the plurality of pixels found in the area of interest Thus, thearrays can be respectively indexed in the data structure according to asequence of the plurality of elements for the media, with each arraycorresponding to a particular element of the plurality of media elementsfound in the sequence. That is to say, the industrial process monitoringmodule 900 can arrange the arrays in a sequential order in the datastructure, such that a later array in the data structure corresponds toa media element occurring later in the media than a media elementcorresponding to an earlier array in the data structure. In additionalor alternative embodiments, the industrial process monitoring module 900arranges the arrays in the data structure with an index indicatingorder, as opposed to being sequentially ordered in the data structure.For example, the industrial process monitoring module 900 can store thedata structure in a JSON format with a field indicating media elementorder for each array.

At Operation 916, the industrial process monitoring module 900 conductsan analysis of the data structure to provide data (information) on oneor more processing parameters associated with the industrial process. Insome instances, the processing parameters may involve parametersassociated with movement of a component of equipment (e.g., a machine).For example, a processing parameter may involve a drive force setting, aspeed setting, a movement range setting, and/or the like for thecomponent. Additionally or alternatively, the processing parameters mayinvolve parameters associated with movement of items handled within theindustrial process. For example, a processing parameter may involve aplacement angle, movement speed, process alignment, and/or the like ofthe items as the items progress through the industrial process.Additionally or alternatively, the processing parameters may involveparameters associated with properties of the items handed within theindustrial process, and/or the like. For example, a processing parametermay involve a surface quality, a paint color, a reflective measure, atemperature, and/or the like of the items as the items progress throughthe industrial process.

In particular embodiments, the industrial process monitoring module 900conducts the analysis by facilitating generation and transmission of agraphical representation of the data structure to a user device fordisplay. For example, the industrial process monitoring module 900 mayfacilitate generation and transmission of a graphical representationthat is similar to the graphical representations 110, 210, 310, 410, 510shown in FIGS. 1G, 2G, 3G, 4G, and 5G by providing a visualrepresentation of each array, with each array arranged substantiallyparallel to a first axis of the graphical representation, and arrangedat least substantially sequentially along a second axis of the graphicalrepresentation according its respective index of the arrays.

In some embodiments, the industrial process monitoring module 900provides the graphical representation to an operator for viewing. Thiscan allow the operator to readily discern how an object, such as acomponent of a machine and/or an item being handled within theindustrial process, moves throughout a cycle, and/or determine whetherthere is a deviation from an expected movement (e.g., the object's rangeof motion, the object's movement timing, the object's location atcertain times in the movement, etc.). In addition, the industrialprocess monitoring module 900 providing the graphical representation toan operator for viewing can allow the operator to readily discern achange in a property of objects, such as an items being handled withinan industrial process, as the items progress through the industrialprocess. Accordingly, the operator can then take one or more actions toaddress the change in the property. Further, such a graphicalrepresentation can assist an operator in optimizing processes and/or thehandling of items, such as assist the operator in identifying timingsequences that can improve processing speed, identifying adjustment inplacement of items within an industrial process that can improvemanufacturing quality, and/or the like.

In addition, the industrial process monitoring module 900 providing thegraphical representation, along with graphical representations generatedfor other points in time of the industrial process, can providesynchronized views of the process with respect to time in that theindustrial process monitoring module 900 can generate the differentgraphical representations for different points in time from datastructures that are produced from the same plurality of media elementsgathered through the same area of interest (e.g., the same field ofview) for the different points in time. Therefore, in some instances,the industrial process monitoring module 900 can allow for an operatorto readily detect variations in the industrial process and/or itemsmanufactured through the industrial process, as well as detectvariations in properties of items manufactured through the industrialprocess.

Alternatively or additionally, the industrial process monitoring module900 conducts the analysis of the data structure (and/or graphicalrepresentation) by providing additional context that may aid an operatorin noticing deviations in one or more objects (e.g., deviations inmovement of one or more machine components and/or items), as well asdeviations in one or more attributes of one or more objects. In someembodiments, the industrial process monitoring module 900 retrieves atemplate (e.g., master) data structure representing baseline attributevalues. For example, the template data may represent an “ideal” oras-designed movement of an object. The industrial process monitoringmodule 900 may calculate a difference data structure by subtracting thedata structure from the template data structure. For example, theindustrial process monitoring module 900 may conduct an object-wisesubtraction of the data structure and the template data structure tocalculate the difference data structure.

In some embodiments, the industrial process monitoring module 900 mayprovide a feature analysis of the difference data structure (e.g.,graphical blob analysis). In additional or alternative embodiments, theindustrial process monitoring module 900 may facilitate transmission ofa graphical representation of the difference data structure to the userdevice for display. Here, the graphical representation of the differencedata structure may provide an operator with a readily ascertainable,visual indication of deviations in object movement from a baseline.

Alternatively or additionally, the industrial process monitoring module900 conducts the analysis by facilitating generation and transmission ofan attribute profile and/or a correlated mapped profile based on thedata structure to a system (e.g., QCS) for the purpose of controllingone or more processing parameters of an industrial process. For example,the industrial process monitoring module 900 may facilitate generationand transmission of an attribute profile and/or mapped profile that aresimilar to the attribute profile 700 and mapped profile 800 shown inFIGS. 7 and 8 , respectively.

In instances where the industrial process monitoring module 900 is beingused for this purpose, the industrial process monitoring module 900 mayprocess media, or a portion thereof, having limited data (e.g., frames,images, and/or the like) on the processing region. For example, theindustrial process monitoring module 900 may process a media elementsuch as a single frame, image, etc. Therefore, the industrial processmonitoring module 900 may analyze a limited number of sets of pixels,and the data structure may comprise a limited array of attributes. Insome embodiments, the industrial process monitoring module 900 mayperform Operations 914 and 916 within the iterations so that theseoperations are carried out for each of the plurality of media elements.Here, the industrial process monitoring module 900 may be configured toperform as such so that the industrial process monitoring module 900 canprovide attribute profiles and/or mapped profiles to the system timelierso that the system can use the attribute profiles and/or mapped profilesto control the one or more processing parameters of the industrialprocess in a more-timely (e.g., quicker) fashion.

As previously noted, the data structure includes attribute valuesextracted from a set of pixels found in media recorded of one or moreobjects (e.g., one or more components of equipment and/or items). Theattribute values may be correlated to property measurements used for thepurpose of controlling one or more processing parameters of theindustrial process. For example, in a paper manufacturing process, themoisture on the paper may be measured for the purpose of controlling asteam box to reduce the moisture by increasing the sheet temperature. Inthis example, the attribute values provided in the data structure may betemperature values that can be correlated to moisture measurements thatare typically taken to control the steam box.

In particular embodiments, the data structure, itself, may be consideredthe attribute profile for the attribute. In other embodiments, theindustrial process monitoring module 900 may generate one or moreattribute profiles from the data structure. For example, the datastructure may comprise values for multiple attributes (e.g., brightness,reflectivity, etc.), and the industrial process monitoring module 900may generate an attribute profile for each type of attribute found inthe data structure.

In some embodiments, the industrial process monitoring module 900provides the one or more attributes profiles to the system (e.g., QC S)that is controlling the one or more processing parameters for theindustrial process. Here, the system may then map the one or moreattribute profiles to one or more correlating mapped profiles ofproperty values (e.g., paper moisture) that correlated to the attributevalues found in the one or more attribute profiles (e.g., temperature).The system can then use the one or more mapped profiles in controllingthe one or more processing parameters.

In other embodiments, the industrial process monitoring module 900carries out the mapping of the one or more attribute profiles to the oneor more mapped profiles. In some embodiments, the industrial processmonitoring module 900 may use a rules-based model in mapping theattribute values found in the one or more attribute profiles tocorrelated property values for the one or more mapped profiles. Forexample, the rules-based model may make use of one or more tables,graphs, rules sets, and/or the like in identifying the correlatedproperty values for the one or more mapped profiles based on theattribute values provided in the one or more measurement profiles.

In some embodiments, the industrial process monitoring module 900 mayidentify a correlation strength (e.g., a correlation strength value)that identifies how well the attribute values found in the one or moreattribute profiles correlate to the property values found in the one ormore mapped profiles. For example, the rules-based model may provide acorrelation strength for each attribute value based on how well therules-based model is able to “match” an attribute value from anattribute profile to a property value for a mapped profile. Theindustrial process monitoring module 900 may then generate an overallcorrelation strength for the mapped profile by taking the average, mean,median, and/or the like for all the correlation strengths (e.g.,values), or the industrial process monitoring module 900 may provide allof the correlation strengths along with the mapped profile. Accordingly,the system may then use the correlation strength in determining whetherto use a particular mapped profile in controlling the one or moreprocessing parameters.

Continuing on, alternatively or additionally, the industrial processmonitoring module 900 conducts the analysis to aid in optimizing anindustrial process by altering the industrial process in real time. Insome embodiments, the industrial process monitoring module 900 may useone or more mapped profiles in the same manner as the system (e.g. QSC)in controlling one or more processing parameters of the industrialprocess. In other embodiments, the industrial process monitoring module900 may modify the industrial process based on determining that anaspect of the difference data structure, previously discussed, exceeds athreshold.

As a specific example, an aspect of the difference data structure mayinclude a timing delay of a periodic movement of an object (e.g.,machine component and/or item) of the industrial process in comparisonto a baseline periodic movement of the object. Here, the periodicmovement of the object may be the placing of a label on a bottle and thetiming delay may involve the placing of the label on a set of bottleswhich resulted in the label being misplaced on the set of bottles.Therefore, in this example, the industrial process monitoring module 900may cause a modification to be made to the industrial process byfacilitating discarding the set of bottles that were produced during thetiming delay. In some embodiments, the industrial process monitoringmodule 900 can facilitate adjusting a processing parameter of theindustrial process, such as a driving force, speed, etc.

Alternatively or additionally, the industrial process monitoring module900 conducts the analysis of the data structure (and/or a graphicalrepresentation) to facilitate operator review by identifying a locationof an object (e.g., machine component and/or item) for the industrialprocess in each of a plurality of arrays. In some embodiments, theindustrial process monitoring module 900 may conduct the analysis byidentifying an object based on a transition in brightness. For example,referencing FIG. 1G, the industrial process monitoring module 900 mayidentify the edge of the arm 106 based on a transition from white toblack in an array.

In additional or alternative embodiments, the industrial processmonitoring module 900 may conduct the analysis by constructing a datasetcomprising the locations and corresponding times. For example,referencing FIG. 1G, the industrial process monitoring module 900 mayconstruct a dataset with a sequence of positions of the arm 106 alongthe vertical axis. In a more complex scenario, the industrial processmonitoring module 900 may construct the dataset to include a position(i.e., pixel) along the vertical axis of a brightest pixel, a darkestpixel, or a brightness gradient indicating an edge of a moving objectversus time.

Alternatively or additionally, the industrial process monitoring module900 conducts the analysis by determining a frequency of movement of theobject by performing a Fourier transform on the dataset. For example, anoperator may use the determined frequency in diagnosing and optimizingthe industrial process by determining a vibration frequency of a machinecomponent. In some embodiments, the industrial process monitoring module900 may also, or instead, conduct the analysis by verifying a graphicalrepresentation against other graphical representations, or alternativelyverifying a data structure against other data structures, correspondingto various operating conditions, thus further aiding diagnosis andoptimization of the industrial process.

Alternatively or additionally, the industrial process monitoring module900 conducts the analysis by sampling the pixels for an array to providefurther data on one or more processing parameters associated with anindustrial process. For example, turning to FIG. 10 , the industrialprocess monitoring module 900 may perform an averaging of valuesrecorded for various pixels over a plurality of arrays found in a datastructure. Here, the averaging is shown in the horizonal position.Therefore, the area of interest 1010 captured in media 1000 is shown asa data structure having a plurality of arrays organized in m rows and ncolumns with each array representing a particular media element (e.g.,particular frame and/or image) recorded for the area of interest 1010 inthe media 1000. Therefore, the industrial process monitoring module 900may generate, for each row of pixels (m), an average attribute value,such as brightness, of all the pixels that belong to the row (m) acrossthe plurality of arrays. The result is an averaged array 1020 havingeach resulting average attribute value generated for each row (m)provided as a value of a single pixel, with the length of the averagedarray 1020 equal to the number of rows (m) in the data structure.Accordingly, the averaged array 1020 can represent the attribute valuesof the entire data structure. Such an averaged array 1020 may be used byan operator in conducting further analysis on the industrial process.

In another example, shown in FIG. 11 , the industrial process monitoringmodule 900 may perform an averaging of values recorded for variouspixels over a particular array representing a particular media elementfound in a plurality of data structures. Again, the averaging is shownin the horizontal position. The area of interest has been captured in aplurality of media 1100A, 1100B, 1100C in which a data structure hasproduced for each media 1100A, 1100B, 1100C. Here, each data structuresincludes a particular array 1110 having m pixels representing aparticular media element captured in the corresponding media 1100A,1100B, 1100C. An n set of the particular array 1110 is provided acrossthe plurality of media 1100A, 1100B, 100C. Therefore, the industrialprocess monitoring module 900 generates, for each location ofcorresponding pixels found within the n set of the particular array1110, an average attribute value, such as brightness, of all thecorresponding pixels that belong to each of the particular arrays 1110across the n set of particular arrays 1110. The result is an averagedarray 1120 having each resulting average attribute value generated foreach location of corresponding pixels provided as a value of a singlepixel, with the length of the averaged array 1120 equal to the number ofpixels (m) in the particular array 1110. Accordingly, the averaged array1120 can represent the attribute values of the entire data structuresgenerated for the plurality of media 1100A, 1100B, 1100C recorded forthe entire area of interest. Again, such an averaged array 1120 may beused by an operator in conducting further analysis on the industrialprocess.

In some instances, the media may include processing portions andinterstitial portions such as, for example, portions where a componentor item is moving, and portions where the component does not move, or noitems are present. In a periodic process, the media may capturealternating processing and interstitial portions (e.g.,processing-interstitial-processing-interstitial-etc.). In theseinstances, the industrial process monitoring module 900 may process theinterstitial portions to introduce arrays that do not necessarilycontain meaningful data and/or obscure underlying, meaningful datarepresenting a component and/or item movement that is useful indiagnosing and analyzing a process. Therefore, in some embodiments, theindustrial process monitoring module 900 may remove the interstitialportions of media by determining a beginning media element (e.g., frame,image, and/or the like) of an interstitial portion and an ending mediaelement of the interstitial portion, and excluding such media elementsfrom the plurality of elements analyzed during Operations 908-914.

For example, the examined industrial process may be periodic (e.g., theprocess may have a processing portion, followed by an interstitialportion, and then another processing portion). Here, the industrialprocess monitoring module 900 may determine a beginning media element ofan interstitial portion based at least in part on receiving a firsttrigger signal indicating an ending of a movement cycle of an object ofthe industrial process. Further, the industrial process monitoringmodule 900 may determine an ending frame of the interstitial portionbased at least in part on receiving a second trigger signal indicating abeginning of the movement cycle of the object of the industrial process.The industrial process monitoring module 900 may then exclude the mediaelements between the beginning media element and the ending mediaelement from the media elements analyzed during Operations 908-914.

Alternatively or additionally, the industrial process monitoring module900 may determine the beginning and ending media elements of aninterstitial portion based at least in part on features identifiedwithin the media itself. For example, the industrial process monitoringmodule 900 may involve determining a beginning media element of aninterstitial portion by detecting a first change in an attribute valueof a particular pixel. As a specific example, such as a change canindicate that an object has returned to a beginning (“home”) position,or such a change can indicate that an item is no longer in a processingregion within the field of view. Similarly, the industrial processmonitoring module 900 may determine an ending media element of theinterstitial portion by detecting a second change in the attribute valueof the particular pixel. For example, such as a change can indicate thatan object has started movement away from the beginning position, or sucha change can indicate that an item has entered the processing region.Depending on the embodiment, the industrial process monitoring module900 may determine the first change and/or the second change based onpixel attributes either inside or outside of the area of interest. Forexample, a first area of interest may indicate a beginning and an endingof a processing cycle, and the industrial process monitoring module 900analyzes pixel attributes from a second area of interest.

In some instances, the industrial process monitoring module 900 mayanalyze a periodic process where the second change (indicating an endingof an interstitial portion) indicates a beginning media element of aprocessing portion, and the first change (indicating a beginning of theinterstitial portion) indicates an ending media element of theprocessing portion. Here, the industrial process monitoring module 900may determine an elapsed time of the processing portion, for instancebased on a property such as a frame rate of the media, a number offrames between the second change and the first change, and/or the like.

In some instances, the industrial process monitoring module 900 mayanalyze a periodic process where a duration of a processing portion mayvary. For instance, a manufacturer may set a manufacturing speed basedon a desired output rate of a final item. When this occurs, media, sucha video with a constant frame rate, may capture more frames than wouldbe captured for a faster manufacturing speed. For example, a videocapturing frames at sixty frames per second can capture twice as manyframes as a result of a change in processing speed causing a process totake two seconds, rather than one second. Without correction, such casescan, for example, lead to an altered graphical representation beingdisplayed, potentially leading to an operator mistakenly suspecting adeviation from a baseline movement.

In various embodiments, the industrial process monitoring module 900 canaddress these cases by removing (downsampling) or adding (upsampling)media elements (e.g., frames, images, and/or the like) corresponding toa processing portion based on an elapsed time. For example, theindustrial process monitoring module 900 can remove elements from theplurality of media elements in response to the elapsed time exceeding abaseline processing time (e.g., the industrial process monitoring module900 can remove every other media element in response to the elapsed timebeing twice as long as a baseline processing time). Alternatively, theindustrial process monitoring module 900 can add elements to theplurality of media elements in response to the elapsed time being lessthan the baseline processing time (e.g., the industrial processmonitoring module 900 can duplicate every media element, and incorporatethe duplicated media elements into the data structure adjacent theoriginal media element, in response to the elapsed time being half aslong as a baseline processing time).

In some embodiments, the industrial process monitoring module 900 mayperform Operation 902 by receiving the media at least substantially inreal time. In these embodiments, rather than upsampling or downsamplingthe media, the industrial process monitoring module 900 may alter aproperty of the device (e.g., camera) providing the media such as, forexample, altering the camera frame rate. For example, the industrialprocess monitoring module 900 may receive a speed measurement indicatinga speed at which an object of the industrial process is moving (e.g., aspeed of an object such as a conveyer). Here, the industrial processmonitoring module 900 may adjust a frame rate of the camera based on adifference between the speed measurement and a baseline speed. In thismanner, the industrial process monitoring module 900 can capture asubstantially equal number of media elements for each processing cycle,regardless of processing speed.

Movements of machine components during a process may obscure or exposelight. Changes in light and shadows may hide features in the movement ofa component or item in the industrial process. For example, returning toFIGS. 1A-1F, a shadow covering the lower half of the field of view maypreclude identification of arm movements below horizontal.

Therefore, in particular embodiments, the industrial process monitoringmodule 900 can identify a control area of the field of view thatcomprises control pixels corresponding to a non-moving object of theindustrial process. For example, the control area may include pixelsthat do not fall in a shadow, or pixels that are in a shadowsimultaneous with an object of interest. In some embodiments, theindustrial process monitoring module 900 can determine a metric ofrespective attribute values of the control pixels over a set of mediaelements corresponding to a movement cycle of an object of theindustrial process, such as an average brightness. In addition, theindustrial process monitoring module 900 can include calibrating theattribute values of the plurality of pixels corresponding to the area ofinterest based on the metric, for instance, by subtracting or scalingattribute values of pixels within an area of interest based on themetric of the control pixels. In this manner, the industrial processmonitoring module 900 can allow accurate representations of movements tobe obtained despite variations in illumination.

Example Computing Hardware

FIG. 12 illustrates a diagrammatic representation of a computerarchitecture of computing hardware 1200 that may be used in practicingvarious embodiments of the present disclosure. In particularembodiments, the computing hardware 1200 may be suitable to receiveinput data from various types of devices, sensors, etc., as well asstore, process, and transmit data.

In particular embodiments, the computing hardware 1200 may be connected(e.g., networked) to one or more other computers using Bluetooth, NFC,another form of short-range wireless communications, and/or otherwireless communications technologies. The computing hardware 1200 mayalso, or instead, be communicatively connected to one or more othercomputers using a physical connection and/or cable (e.g., USB, mini-USB,micro-USB, standard USB of any type, etc.). The computing hardware 1200may also, or instead, connect to other computers using a LAN, anintranet, an extranet, and/or the Internet (e.g., using any wired and/orwireless communications connection). The computing hardware 1200 may be,or may be based on, any type of device having one or more processors anddata storage capabilities and capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatcomputer. Further, while only a single computer is illustrated, the term“computer” shall also be taken to include any collection of computersthat individually or jointly execute a set (or multiple sets) ofinstructions to perform any one or more of the methodologies discussedherein, such as the data compression and/or decompression methodsdescribed in more detail below.

The computing hardware 1200 may include a processing device 1202 (e.g.,one or more computer processors) and a main memory 1204 (e.g., read-onlymemory (ROM), random access memory (RAM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM(RDRAM), etc.) storing instructions 1222 that may be executed by theprocessing device 1202. The computing hardware 1200 may also include astatic memory 1206 (e.g., flash memory, static random-access memory(SRAM), etc.) and a data storage device 1218. All such components of thecomputing hardware 1200 may communicate with each other via a bus 1228.

The processing device 1202 represents one or more general-purposeprocessing devices such as a microprocessor, a central processing unit,and the like. More particularly, each processing device of theprocessing device 1202 may be a complex instruction set computing (CISC)microprocessor, reduced instruction set computing (RISC) microprocessor,very long instruction word (VLIW) microprocessor, Scalar Board, aprocessor implementing other instruction sets, or a processorimplementing a combination of instruction sets. Each processing deviceof the processing device 1202 may also, or instead, be one or morespecial-purpose processing devices such as an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA), adigital signal processor (DSP), a network processor, and the like. Theprocessing device 1202 may be configured to execute processing logic1226 for performing various operations and steps discussed herein.

The computing hardware 1200 may further include a network interfacedevice 1208 that may include one or more NFC components, Bluetoothcomponents, any other type of short-range wireless communicationscomponents, and/or any other wireless communications components that mayallow communication directly with any other device and/or via any typeof network. The network interface device 1108 may also, or instead,include one or more wired communications components that may facilitatewired communications via a physical connection to one or more otherdevices (e.g., USB, mini-USB, micro-USB, standard USB of any type,etc.). The computing hardware 1200 also may include a video display unit1210 (e.g., a flexible computer display, a liquid crystal display (LCD),an LED display, or any other suitable display), an alphanumeric or othertype of input device 1212 (e.g., a keyboard, a touchscreen, etc.), acursor control or other input device 1214 (e.g., touch-sensitive inputdevice, or other suitable input device, etc.), and a signal generationdevice 1216 (e.g., a speaker, function generator, etc.).

The data storage device 1218 may include a non-transitorycomputer-accessible storage medium 1220 (also known as a non-transitorycomputer-readable storage medium or a non-transitory computer-readablemedium) on which may be stored one or more sets of instructions 1222(e.g., software) embodying any one or more of the methodologies orfunctions such as the industrial process monitoring module 900 asdescribed herein. The instructions 1222 may also reside, completely orat least partially, within the main memory 1204 and/or within theprocessing device 1202 during execution thereof by the computinghardware 1200. The main memory 1204 and the processing device 1202 mayalso constitute computer-accessible storage media. The instructions 1222may further be transmitted or received directly from another deviceand/or over a network (e.g., one or more networks 1224) via the networkinterface device 1208.

While the computer-accessible storage medium 1220 is shown in anexemplary embodiment to be a single medium, the terms“computer-accessible storage medium,” “computer-readable storagemedium,” and “computer-readable medium” should be understood to includea single medium or multiple media (e.g., a centralized or distributeddatabase and/or associated caches and servers) that store the one ormore sets of instructions. The terms “computer-accessible storagemedium,” “computer-readable storage medium,” and “computer-readablemedium” should also be understood to include any medium that is capableof storing, encoding, or carrying a set of instructions for execution bythe computer and that cause the computer to perform any one or more ofthe methodologies of the present invention. The terms“computer-accessible storage medium,” “computer-readable storagemedium,” and “computer-readable medium” should accordingly be understoodto include, but not be limited to, solid-state memories, optical media,magnetic media, etc.

Also, while the computing hardware 1200 is shown in FIG. 12 as includingvarious components, it should be understood that the computing hardware1200 may include greater or fewer components in other embodiments. Forexample, in certain embodiments, the computing hardware 1200 may notinclude a video display unit 1210, signal generation device 1216, orother components shown in FIG. 12 .

Example System Architecture

FIG. 13 is a diagram illustrating an example of a system architecture1300 in which various embodiments of the disclosure may be implemented.As shown in FIG. 13 , the system architecture 1300 may include recordingequipment 1302 such as, for example, an area scan camera, a line scancamera, an infrared camera, and/or the like that is pointed at a dynamicprocessing region 1304. For example, the dynamic processing region 1304may include an area, location, and/or the like of an industrial processwhere an item 1306 handled within the industrial process is transferredalong a path 1308 and processed within a field of view of the recordingequipment 1302.

In various embodiments, computing hardware 1200 may execute theindustrial process monitoring module 900, as described herein, tomonitor aspects of the industrial process via an area of interest 1310within the field of view (e.g., manipulation of the item 1306).Accordingly, the area of interest 1310 may be based on the recordingequipment's view and process behavior being monitored. For example, thearea of interest 1310 may be based on encapsulating motion of amonitored object (e.g., the item 1306 and/or a component of a machine)while avoiding interference from inconsequential motion.

The system architecture 1300 may also include other components such as,for example, a speed encoder 1312 for measuring movement of the item1306, an acquisition start trigger 1314, and/or an acquisition endtrigger 1316. For example, the acquisition start trigger 1314 and/or theacquisition end trigger 1316 may include a Hall effect sensor, sonicproximity sensor, laser proximity sensor, continuity or voltage sensor,etc. In some embodiments, data from the speed encoder 1312 may be usedto control the frame rate of the recording equipment 1302, frequency ofthe recording equipment 1302, and/or the like to facilitatevisualization of the process in substantially equal increments ofdistance traveled by the item 1306.

The acquisition start trigger 1314 and/or acquisition end trigger 1316may be connected to the computing hardware 1200 to facilitate thecomputing hardware 1200 in capturing processing portions and excludinginterstitial portions of video. Further, an output module 1320 mayprovide results of process verification to other systems (e.g. QC S),process controls, and/o the like, as well as personnel, to alterprocessing parameters of the industrial process that may lie upstreamand/or downstream of the recording equipment 1302. The systemarchitecture 1300 may further include a light 1322 to aid in constantand even illumination. As described above, the computing hardware 1200may be configured to execute the industrial process monitoring module900 without input from the acquisition start trigger 1314 and/or theacquisition end trigger 1316 (e.g., using features of the captured videoto identify a start and stop of a process).

In certain embodiments, resolution of the recording equipment 1302 maybe set to a high resolution for a given model and frame rate. In someinstances, the pixel resolution and field of view may influence theresolution of a graphical representation and/or a measurement profile.For instance, a smaller field of view and/or higher pixel resolution mayresult in higher spatial resolution of the graphical representationand/or measurement profile. Other recording equipment settings such asgain, exposure, etc., may be set to maximize the ability to monitor theindustrial process within the view of the recording equipment 1302.

CONCLUSION

It should be understood that various aspects of the system architecturedescribed above may be applicable to other types of systemarchitectures, in general. While this specification contains manyspecific embodiment details, these should not be construed aslimitations on the scope of any invention or of what may be claimed, butrather as descriptions of features that may be specific to particularembodiments of particular inventions. Certain features that aredescribed in this specification in the context of separate embodimentsmay also be implemented in combination in a single embodiment.Conversely, various features that are described in the context of asingle embodiment may also be implemented in multiple embodimentsseparately or in any suitable sub-combination. Moreover, althoughfeatures may be described above as acting in certain combinations andeven initially claimed as such, one or more features from a claimedcombination may in some cases be excised from the combination, and theclaimed combination may be directed to a sub-combination or variation ofa sub-combination.

Similarly, while operations may be described in a particular order, thisshould not be understood as requiring that such operations be performedin the particular order described or in sequential order, or that alldescribed operations be performed, to achieve desirable results. Incertain circumstances, multitasking and parallel processing may beadvantageous. Moreover, the separation of various system components inthe embodiments described above should not be understood as requiringsuch separation in all embodiments, and it should be understood that thedescribed program components and systems may generally be integratedtogether in a single software product or packaged into multiple softwareproducts.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains havingthe benefit of the teachings presented in the foregoing descriptions andthe associated drawings. Therefore, it is to be understood that theinvention is not to be limited to the specific embodiments disclosed andthat modifications and other embodiments are intended to be includedwithin the scope of the appended claims. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for the purposes of limitation.

What is claimed:
 1. A method comprising: receiving, by computinghardware, media of a processing region of an industrial process,wherein: the processing region comprises at least one object, the mediacomprises a plurality of media elements, and each media element of theplurality of media elements comprises a field of view of the at leastone object; identifying, by the computing hardware and based on an areaof interest, a set of pixels, wherein the field of view comprises thearea of interest; and for each media element of the plurality of mediaelements: extracting, by the computing hardware, an attribute value fromeach pixel of the set of pixels found in the media element;constructing, by the computing hardware, an attribute profile comprisingthe attribute value for each pixel of the set of pixels; mapping, by thecomputing hardware, the attribute profile to a mapped profile, whereinthe mapped profile comprises at least one property value that correlatesto at least one attribute value of the attribute profile; and providing,by the computing hardware, the mapped profile to a control system,wherein the control system uses the mapped profile in controlling one ormore processing parameters of the industrial process, wherein theindustrial process comprises a manufacturing process for paper, the atleast one attribute value comprises a measure of brightness of at leastone pixel of the set of pixels, the at least one property valuecomprises a measure of a thickness of the paper, and the one or moreprocessing parameters comprise an amount of pulp fed by one or moreactuators during the manufacturing of the paper.
 2. A method comprising:receiving, by computing hardware, media of a processing region of anindustrial process, wherein: the processing region comprises at leastone object, the media comprises a plurality of media elements, and eachmedia element of the plurality of media elements comprises a field ofview of the at least one object; identifying, by the computing hardwareand based on an area of interest, a set of pixels, wherein the field ofview comprises the area of interest; and for each media element of theplurality of media elements: extracting, by the computing hardware, anattribute value from each pixel of the set of pixels found in the mediaelement; constructing, by the computing hardware, an attribute profilecomprising the attribute value for each pixel of the set of pixels;mapping, by the computing hardware, the attribute profile to a mappedprofile, wherein the mapped profile comprises at least one propertyvalue that correlates to at least one attribute value of the attributeprofile; and providing, by the computing hardware, the mapped profile toa control system, wherein the control system uses the mapped profile incontrolling one or more processing parameters of the industrial process,wherein the industrial process comprises a manufacturing process forpaper, the at least one attribute value comprises a measure oftemperature of at least one pixel of the set of pixels, the at least oneproperty value comprises a measure of moisture of the paper, and the oneor more processing parameters comprise an amount of steam provided byone or more steam boxes to a surface of the paper during themanufacturing of the paper.
 3. The method of claim 1 further comprising:averaging, by the computing hardware, the at least one attribute valuefound in the attribute profile constructed for each media element of theplurality of media elements in a time domain to produce an averageattribute value; and analyzing, by the computing hardware, the averageattribute value to determine a variation in the one or more processingparameters of the industrial process.
 4. The method of claim 1, whereinmapping the attribute profile to the mapped profile comprises using arules-based model to map the at least one attribute value to the atleast one property value, and the rules-based model uses at least one ofa table, graph, or rules sets in identifying the at least one propertyvalue.
 5. The method of claim 1 further comprising: identifying, by thecomputing hardware, a correlation strength that identifies how well theat least one attribute value correlates to the at least one propertyvalue; and providing, by the computing hardware, the correlationstrength along with the mapped profile to the control system, whereinthe control system determines, based on the correlation strength, to usethe mapped profile in controlling the one or more processing parametersof the industrial process.
 6. The method of claim 1, wherein the atleast one object comprises at least one of a component of equipment oran item being manufactured.
 7. A system comprising: a non-transitorycomputer-readable medium storing instructions; and a processing devicecommunicatively coupled to the non-transitory computer-readable medium,wherein, the processing device is configured to execute the instructionsand thereby perform operations comprising: receiving a media element ofa processing region of an industrial process, wherein: the processingregion comprises at least one object, and the media element comprises afield of view of the at least one object; identifying, based on an areaof interest, a set of pixels, wherein the field of view comprises thearea of interest; and extracting an attribute value from each pixel ofthe set of pixels found in the media element; constructing an attributeprofile comprising the attribute value for each pixel of the set ofpixels; and mapping the attribute profile to a mapped profile, whereinthe mapped profile comprises at least one property value that correlatesto at least one attribute value of the attribute profile, and the atleast one property value is used by a control system in controlling oneor more processing parameters of the industrial process, wherein theindustrial process comprises a manufacturing process for paper, the atleast one attribute value comprises a measure of temperature of at leastone pixel of the set of pixels, the at least one property valuecomprises a measure of moisture of the paper, and the one or moreprocessing parameters comprise an amount of steam provided by one ormore steam boxes to a surface of the paper during the manufacturing ofthe paper.
 8. The system of claim 7, wherein the operations furthercomprise providing the mapped profile to the control system to use themapped profile in controlling the one or more processing parameters ofthe industrial process.
 9. A system comprising: a non-transitorycomputer-readable medium storing instructions; and a processing devicecommunicatively coupled to the non-transitory computer-readable medium,wherein, the processing device is configured to execute the instructionsand thereby perform operations comprising: receiving a media element ofa processing region of an industrial process, wherein: the processingregion comprises at least one object, and the media element comprises afield of view of the at least one object; identifying, based on an areaof interest, a set of pixels, wherein the field of view comprises thearea of interest; and extracting an attribute value from each pixel ofthe set of pixels found in the media element; constructing an attributeprofile comprising the attribute value for each pixel of the set ofpixels; and mapping the attribute profile to a mapped profile, whereinthe mapped profile comprises at least one property value that correlatesto at least one attribute value of the attribute profile, and the atleast one property value is used by a control system in controlling oneor more processing parameters of the industrial process, wherein theindustrial process comprises a manufacturing process for paper, the atleast one attribute value comprises a measure of brightness of at leastone pixel of the set of pixels, the at least one property valuecomprises a measure of a thickness of the paper, and the one or moreprocessing parameters comprise an amount of pulp fed by one or moreactuators during the manufacturing of the paper.
 10. The system of claim7, wherein mapping the attribute profile to the mapped profile comprisesusing a rules-based model to map the at least one attribute value to theat least one property value.
 11. The system of claim 7, wherein theoperations further comprise: identifying a correlation strength thatidentifies how well the at least one attribute value correlates to theat least one property value; and providing the correlation strengthalong with the mapped profile to the control system, wherein the controlsystem determines, based on the correlation strength, to use the mappedprofile in controlling the one or more processing parameters of theindustrial process.
 12. A non-transitory computer-readable mediumstoring computer-executable instructions that, when executed bycomputing hardware, configure the computing hardware to performoperations comprising: receiving a media element of a processing regionof an industrial process, wherein: the processing region comprises atleast one object, and the media element comprises a field of view of theat least one object; identifying, based on an area of interest, a set ofpixels, wherein the field of view comprises the area of interest;extracting an attribute value from each pixel of the set of pixels foundin the media element; and mapping at least one attribute value for atleast one pixel of the set of pixels to a mapped profile, wherein themapped profile comprises at least one property value that correlates tothe at least one attribute value, and the at least one property value isused by a control system in controlling one or more processingparameters of the industrial process, wherein the industrial processcomprises a manufacturing process for paper, the at least one attributevalue comprises a measure of brightness of at least one pixel of the setof pixels, the at least one property value comprises a measure of athickness of the paper, and the one or more processing parameterscomprise an amount of pulp fed by one or more actuators during themanufacturing of the paper.
 13. The non-transitory computer-readablemedium of claim 12, wherein the operations further comprise providingthe mapped profile to the control system to use the mapped profile incontrolling the one or more processing parameters of the industrialprocess.
 14. The non-transitory computer-readable medium of claim 12,wherein mapping the at least one attribute value to the mapped profilecomprises using a rules-based model to map the at least one attributevalue to the at least one property value.
 15. The non-transitorycomputer-readable medium of claim 12, wherein the operations furthercomprise: identifying a correlation strength that identifies how wellthe at least one attribute value correlates to the at least one propertyvalue; and providing the correlation strength along with the mappedprofile to the control system, wherein the control system determines,based on the correlation strength, to use the mapped profile incontrolling the one or more processing parameters of the industrialprocess.
 16. A non-transitory computer-readable medium storingcomputer-executable instructions that, when executed by computinghardware, configure the computing hardware to perform operationscomprising: receiving a media element of a processing region of anindustrial process, wherein: the processing region comprises at leastone object, and the media element comprises a field of view of the atleast one object; identifying, based on an area of interest, a set ofpixels, wherein the field of view comprises the area of interest;extracting an attribute value from each pixel of the set of pixels foundin the media element; and mapping at least one attribute value for atleast one pixel of the set of pixels to a mapped profile, wherein themapped profile comprises at least one property value that correlates tothe at least one attribute value, and the at least one property value isused by a control system in controlling one or more processingparameters of the industrial process, wherein the industrial processcomprises a manufacturing process for paper, the at least one attributevalue comprises a measure of temperature of at least one pixel of theset of pixels, the at least one property value comprises a measure ofmoisture of the paper, and the one or more processing parameterscomprise an amount of steam provided by one or more steam boxes to asurface of the paper during the manufacturing of the paper.
 17. Thenon-transitory computer-readable medium of claim 16, wherein theoperations further comprise providing the mapped profile to the controlsystem to use the mapped profile in controlling the one or moreprocessing parameters of the industrial process.
 18. The non-transitorycomputer-readable medium of claim 16, wherein the operations furthercomprise: identifying a correlation strength that identifies how wellthe at least one attribute value correlates to the at least one propertyvalue; and providing the correlation strength along with the mappedprofile to the control system, wherein the control system determines,based on the correlation strength, to use the mapped profile incontrolling the one or more processing parameters of the industrialprocess.
 19. The non-transitory computer-readable medium of claim 16,wherein mapping the at least one attribute value to the mapped profilecomprises using a rules-based model to map the at least one attributevalue to the at least one property value.